The need for research instruments adaptable to culturally diverse populations has grown with globalization and digital connectivity. Ensuring measurement invariance (MI) is crucial for generating accurate and comparable scores, especially in comparative studies. Traditional approaches like Multi-Group Confirmatory Factor Analysis (MG-CFA) often involve intricate procedures and can become unwieldy when adjustments for partial invariance are needed. The Alignment-within-CFA (AwC) method emerged as a promising alternative, designed to approximate group-specific factors and produce latent variables with uniform metrics. This study rigorously compares the AwC method and traditional MG-CFA across moderate numbers of groups (3, 4, and 5) under various conditions of noninvariance and sample sizes. By employing Monte Carlo simulations, the study controls study variables and explores a wide range of hypothetical scenarios, enhancing the precision and reliability of MI testing. The findings indicate that the AwC method is similar to or superior to the step-wise partial invariance approach, offering accurate and consistent results in varied scenarios. Specifically, the study examines the conditions under which AwC outperforms traditional MG-CFA and investigates the impact of factors such as different types of invariance, number of groups, and sample size on bias and model fit. This research provides deeper insights into the strengths and limitations of each method, guiding researchers in selecting the most appropriate approach for their specific contexts. The results support the use of the AwC method in scenarios where minimizing bias and error in parameter estimates is critical, paving the way for more streamlined and effective research amidst increasing global diversity.